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Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression.

Publication ,  Journal Article
Messier, KP; Chambliss, SE; Gani, S; Alvarez, R; Brauer, M; Choi, JJ; Hamburg, SP; Kerckhoffs, J; LaFranchi, B; Lunden, MM; Marshall, JD ...
Published in: Environmental science & technology
November 2018

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R2 than the LUR-K approach within 4 to 8 repeated drive days per road segment.

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Published In

Environmental science & technology

DOI

EISSN

1520-5851

ISSN

0013-936X

Publication Date

November 2018

Volume

52

Issue

21

Start / End Page

12563 / 12572

Related Subject Headings

  • Particulate Matter
  • Environmental Sciences
  • Environmental Monitoring
  • Cities
  • Air Pollution
  • Air Pollutants
 

Citation

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Messier, K. P., Chambliss, S. E., Gani, S., Alvarez, R., Brauer, M., Choi, J. J., … Apte, J. S. (2018). Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression. Environmental Science & Technology, 52(21), 12563–12572. https://doi.org/10.1021/acs.est.8b03395
Messier, Kyle P., Sarah E. Chambliss, Shahzad Gani, Ramon Alvarez, Michael Brauer, Jonathan J. Choi, Steven P. Hamburg, et al. “Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression.Environmental Science & Technology 52, no. 21 (November 2018): 12563–72. https://doi.org/10.1021/acs.est.8b03395.
Messier KP, Chambliss SE, Gani S, Alvarez R, Brauer M, Choi JJ, et al. Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression. Environmental science & technology. 2018 Nov;52(21):12563–72.
Messier, Kyle P., et al. “Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression.Environmental Science & Technology, vol. 52, no. 21, Nov. 2018, pp. 12563–72. Epmc, doi:10.1021/acs.est.8b03395.
Messier KP, Chambliss SE, Gani S, Alvarez R, Brauer M, Choi JJ, Hamburg SP, Kerckhoffs J, LaFranchi B, Lunden MM, Marshall JD, Portier CJ, Roy A, Szpiro AA, Vermeulen RCH, Apte JS. Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression. Environmental science & technology. 2018 Nov;52(21):12563–12572.
Journal cover image

Published In

Environmental science & technology

DOI

EISSN

1520-5851

ISSN

0013-936X

Publication Date

November 2018

Volume

52

Issue

21

Start / End Page

12563 / 12572

Related Subject Headings

  • Particulate Matter
  • Environmental Sciences
  • Environmental Monitoring
  • Cities
  • Air Pollution
  • Air Pollutants